Deep Learning training is available as "onsite live training" or "remote live training". Onsite live Deep Learning training can be carried out locally on customer premises in Vietnam or in NobleProg corporate training centers in Vietnam. Remote live training is carried out by way of an interactive, remote desktop.

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Testimonials

★★★★★

★★★★★

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease

Course: Artificial Neural Networks, Machine Learning, Deep Thinking

The topic is very interesting.

Wojciech Baranowski

Course: Introduction to Deep Learning

Trainers theoretical knowledge and willingness to solve the problems with the participants after the training.

Grzegorz Mianowski

Course: Introduction to Deep Learning

Topic. Very interesting!.

Piotr

Course: Introduction to Deep Learning

Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.

Dolby Poland Sp. z o.o.

Course: Introduction to Deep Learning

I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient.

Radek

Course: Introduction to Deep Learning

The global overview of deep learning.

Bruno Charbonnier

Course: Advanced Deep Learning

The exercises are sufficiently practical and do not need high knowledge in Python to be done.

The subject. It seemed interesting, but I left knowing not much more than before.

Radoslaw Labedzki

Course: Introduction to Deep Learning

I liked that this course had very interesting subject.

Wojciech Wilk

Course: Introduction to Deep Learning

We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company.

Sebastiaan Holman

Course: Machine Learning and Deep Learning

The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.

Deep Learning (DL) Subcategories

DL (Deep Learning) Course Outlines

Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

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have a good understanding on deep neural networks(DNN), CNN and RNN

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understand TensorFlow’s structure and deployment mechanisms

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be able to carry out installation / production environment / architecture tasks and configuration

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be able to assess code quality, perform debugging, monitoring

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be able to implement advanced production like training models, building graphs and logging

Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject.

The Duration of the complete course will be around 70 hours and not 35 hours.

Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team.

In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks.

By the end of this training, participants will be able to:

- Install tensor2tensor, select a data set, and train and evaluate an AI model- Customize a development environment using the tools and components included in Tensor2Tensor- Create and use a single model to concurrently learn a number of tasks from multiple domains- Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited- Obtain satisfactory processing results using a single GPU

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.

This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.

By the end of this training, participants will be able to:

- Explore how data is being interpreted by machine learning models- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.- Explore the properties of a specific embedding to understand the behavior of a model- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

In this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.

TensorFlow Serving is a system for serving machine learning (ML) models to production.

In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

By the end of this training, participants will be able to:

- Train, export and serve various TensorFlow models- Test and deploy algorithms using a single architecture and set of APIs- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Deep Learning for NLP (Natural Language Processing) allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos.

In this instructor-led, live training, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions.

Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks.

In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.

By the end of this training, participants will be able to:

- Access CNTK as a library from within a Python, C#, or C++ program- Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)- Use the CNTK model evaluation functionality from a Java program- Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)- Scale computation capacity on CPUs, GPUs and multiple machines- Access massive datasets using existing programming languages and algorithms

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Note

- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning- Learn the applications and uses of deep learning in finance- Use R to create deep learning models for finance- Build their own deep learning stock price prediction model using R

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning- Learn the applications and uses of deep learning in banking- Use Python, Keras, and TensorFlow to create deep learning models for banking- Build their own deep learning credit risk model using Python

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning- Learn the applications and uses of deep learning in banking- Use R to create deep learning models for banking- Build their own deep learning credit risk model using R

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning- Learn the applications and uses of deep learning in finance- Use Python, Keras, and TensorFlow to create deep learning models for finance- Build their own deep learning stock price prediction model using Python

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.

In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

Deep learning is becoming a principal component of future product design that wants to incorporate artificial intelligence at the heart of their models. Within the next 5 to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. So far Google, Sales Force, Facebook, Amazon have been successfully using deep learning AI to boost their business. Applications ranged from automatic machine translation, image analytics, video analytics, motion analytics, generating targeted advertisement and many more.

This coursework is aimed for those organizations who want to incorporate Deep Learning as very important part of their product or service strategy. Below is the outline of the deep learning course which we can customize for different levels of employees/stakeholders in an organization.

Target Audience:

( Depending on target audience, course materials will be customized)

Executives

A general overview of AI and how it fits into corporate strategy, with breakout sessions on strategic planning, technology roadmaps, and resource allocation to ensure maximum value.

Project Managers

How to plan out an AI project, including data gathering and evaluation, data cleanup and verification, development of a proof-of-concept model, integration into business processes, and delivery across the organization.

Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.

In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations.

By the end of this training, participants will be able to:

- Understand the fundamentals of Deep Learning- Learn Deep Learning techniques and their applications in the industry- Examine issues in medicine which can be solved by Deep Learning technologies- Explore Deep Learning case studies in medicine- Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine

Audience

- Managers- Medical professionals in leadership roles

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Note

- To request a customized training for this course, please contact us to arrange.

In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.

By the end of the training, participants will be able to:

- Train various types of neural networks on large amounts of data- Use TPUs to speed up the inference process by up to two orders of magnitude- Utilize TPUs to process intensive applications such as image search, cloud vision and photos

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.

Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.

TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms- be able to carry out installation / production environment / architecture tasks and configuration- be able to assess code quality, perform debugging, monitoring- be able to implement advanced production like training models, building graphs and logging

TensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms- be able to carry out installation / production environment / architecture tasks and configuration- be able to assess code quality, perform debugging, monitoring- be able to implement advanced production like training models, embedding terms, building graphs and logging

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